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Update doc config
Change build tool to hatch
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8
.github/workflows/main.yml
vendored
8
.github/workflows/main.yml
vendored
@ -13,12 +13,12 @@ jobs:
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strategy:
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strategy:
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matrix:
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matrix:
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os: [macos-latest, ubuntu-latest, windows-latest]
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os: [macos-latest, ubuntu-latest, windows-latest]
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python: [3.8, 3.9, "3.10"]
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python: [3.11, 3.12]
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steps:
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python }}
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- name: Set up Python ${{ matrix.python }}
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uses: actions/setup-python@v4
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uses: actions/setup-python@v5
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with:
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with:
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python-version: ${{ matrix.python }}
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python-version: ${{ matrix.python }}
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- name: Install dependencies
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- name: Install dependencies
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@ -35,7 +35,7 @@ jobs:
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coverage run -m unittest -v odte.tests
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coverage run -m unittest -v odte.tests
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coverage xml
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coverage xml
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- name: Upload coverage to Codecov
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- name: Upload coverage to Codecov
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uses: codecov/codecov-action@v3
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uses: codecov/codecov-action@v4
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with:
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with:
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token: ${{ secrets.CODECOV_TOKEN }}
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token: ${{ secrets.CODECOV_TOKEN }}
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files: ./coverage.xml
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files: ./coverage.xml
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1
MANIFEST.in
Normal file
1
MANIFEST.in
Normal file
@ -0,0 +1 @@
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include README.md LICENSE
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43
Makefile
43
Makefile
@ -1,44 +1,35 @@
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SHELL := /bin/bash
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SHELL := /bin/bash
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.DEFAULT_GOAL := help
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.DEFAULT_GOAL := help
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.PHONY: coverage deps help lint push test doc build
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.PHONY: audit coverage help lint test doc doc-clean build
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coverage: ## Run tests with coverage
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coverage: ## Run tests with coverage
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coverage erase
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@coverage erase
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coverage run -m unittest -v odte.tests
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@coverage run -m unittest -v odte.tests
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coverage report -m
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@coverage report -m
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deps: ## Install dependencies
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lint: ## Lint source files
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pip install -r requirements.txt
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@black odte
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@flake8 odte
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devdeps: ## Install development dependencies
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@mypy odte
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pip install black pip-audit flake8 mypy coverage
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lint: ## Lint and static-check
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black odte
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flake8 odte
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mypy odte --exclude tests
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audit: ## Audit pip
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audit: ## Audit pip
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pip-audit
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@pip-audit
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push: ## Push code with tags
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git push && git push --tags
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test: ## Run tests
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test: ## Run tests
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python -m unittest -v odte.tests
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@python -m unittest -v odte.tests
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doc: ## Update documentation
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doc: ## Update documentation
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make -C docs --makefile=Makefile html
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@make -C docs --makefile=Makefile html
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build: ## Build package
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build: ## Build package
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rm -fr dist/*
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@rm -fr dist/*
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rm -fr build/*
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@rm -fr build/*
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python setup.py sdist bdist_wheel
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@hatch build
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doc-clean: ## Update documentation
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doc-clean: ## Clean documentation folders
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make -C docs --makefile=Makefile clean
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@make -C docs --makefile=Makefile clean
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help: ## Show help message
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help: ## Show this help message
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@IFS=$$'\n' ; \
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@IFS=$$'\n' ; \
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help_lines=(`fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's/##/:/'`); \
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help_lines=(`fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's/##/:/'`); \
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printf "%s\n\n" "Usage: make [task]"; \
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printf "%s\n\n" "Usage: make [task]"; \
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20
docs/Makefile
Normal file
20
docs/Makefile
Normal file
@ -0,0 +1,20 @@
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# Minimal makefile for Sphinx documentation
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#
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# You can set these variables from the command line, and also
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# from the environment for the first two.
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SPHINXOPTS ?=
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SPHINXBUILD ?= sphinx-build
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SOURCEDIR = source
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BUILDDIR = build
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# Put it first so that "make" without argument is like "make help".
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help:
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@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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.PHONY: help Makefile
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# Catch-all target: route all unknown targets to Sphinx using the new
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# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
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%: Makefile
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@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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4
docs/requirements.txt
Normal file
4
docs/requirements.txt
Normal file
@ -0,0 +1,4 @@
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sphinx
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sphinx-rtd-theme
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myst-parser
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stree
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10
docs/source/api/Odte.rst
Normal file
10
docs/source/api/Odte.rst
Normal file
@ -0,0 +1,10 @@
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Odte
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=====
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.. automodule:: odte
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.. autoclass:: Odte
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:members:
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:undoc-members:
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:private-members:
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:show-inheritance:
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:noindex:
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8
docs/source/api/index.rst
Normal file
8
docs/source/api/index.rst
Normal file
@ -0,0 +1,8 @@
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API index
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=========
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.. toctree::
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:maxdepth: 2
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:caption: Contents:
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Odte
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54
docs/source/conf.py
Normal file
54
docs/source/conf.py
Normal file
@ -0,0 +1,54 @@
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# Configuration file for the Sphinx documentation builder.
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#
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# This file only contains a selection of the most common options. For a full
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# list see the documentation:
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# https://www.sphinx-doc.org/en/master/usage/configuration.html
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# -- Path setup --------------------------------------------------------------
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# If extensions (or modules to document with autodoc) are in another directory,
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# add these directories to sys.path here. If the directory is relative to the
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# documentation root, use os.path.abspath to make it absolute, like shown here.
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#
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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import odte
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# -- Project information -----------------------------------------------------
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project = "Odte"
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copyright = "2024 Ricardo Montañana Gómez"
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author = "Ricardo Montañana Gómez"
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# The full version, including alpha/beta/rc tags
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version = release = odte.__version__
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# -- General configuration ---------------------------------------------------
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# Add any Sphinx extension module names here, as strings. They can be
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# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
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# ones.
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extensions = ["myst_parser", "sphinx.ext.autodoc", "sphinx.ext.viewcode"]
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# Add any paths that contain templates here, relative to this directory.
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templates_path = ["_templates"]
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# List of patterns, relative to source directory, that match files and
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# directories to ignore when looking for source files.
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# This pattern also affects html_static_path and html_extra_path.
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exclude_patterns = []
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# -- Options for HTML output -------------------------------------------------
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# The theme to use for HTML and HTML Help pages. See the documentation for
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# a list of builtin themes.
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#
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html_theme = "sphinx_rtd_theme"
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# Add any paths that contain custom static files (such as style sheets) here,
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# relative to this directory. They are copied after the builtin static files,
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# so a file named "default.css" will overwrite the builtin "default.css".
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html_static_path = []
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BIN
docs/source/example.png
Normal file
BIN
docs/source/example.png
Normal file
Binary file not shown.
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After Width: | Height: | Size: 3.1 MiB |
19
docs/source/hyperparameters.md
Normal file
19
docs/source/hyperparameters.md
Normal file
@ -0,0 +1,19 @@
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# Hyperparameters
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| | **Hyperparameter** | **Type/Values** | **Default** | **Meaning** |
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| --- | ------------------- | -------------------------------------------------------------- | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| | estimator | \<sklearn.BaseEstimator\> | Stree() | Base estimator used to build each element of the ensemble. |
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| | n_jobs | \<int\> | -1 | Specifies the number of threads used to build the ensemble (-1 equals to all cores available) |
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| | random_state | \<int\> | None | Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False.<br>Pass an int for reproducible output across multiple function calls |
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|
| | max_features | \<int\>, \<float\> <br><br>or {“auto”, “sqrt”, “log2”} | None | The number of features to consider when looking for the split:<br>If int, then consider max_features features at each split.<br>If float, then max_features is a fraction and int(max_features \* n_features) features are considered at each split.<br>If “auto”, then max_features=sqrt(n_features).<br>If “sqrt”, then max_features=sqrt(n_features).<br>If “log2”, then max_features=log2(n_features).<br>If None, then max_features=n_features. |
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| | max_samples | \<int\> | 3 | Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.|
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| | n_estimators | \<int\> | 100 | The number of trees the ensemble is going to build |
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| | be_hyperparams | \<str\> | "{}" | Hyperparameteres passed to the base estimator, i.e. "{\\"C\\": 17, \\"kernel\\": \\"rbf\\"}"|
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\*\* **Splitting in a STree node**
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The decision function is applied to the dataset and distances from samples to hyperplanes are computed in a matrix. This matrix has as many columns as classes the samples belongs to (if more than two, i.e. multiclass classification) or 1 column if it's a binary class dataset. In binary classification only one hyperplane is computed and therefore only one column is needed to store the distances of the samples to it. If three or more classes are present in the dataset we need as many hyperplanes as classes are there, and therefore one column per hyperplane is needed.
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In case of multiclass classification we have to decide which column take into account to make the split, that depends on hyperparameter _split_criteria_, if "impurity" is chosen then STree computes information gain of every split candidate using each column and chooses the one that maximize the information gain, otherwise STree choses the column with more samples with a predicted class (the column with more positive numbers in it).
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|
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Once we have the column to take into account for the split, the algorithm splits samples with positive distances to hyperplane from the rest.
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14
docs/source/index.rst
Normal file
14
docs/source/index.rst
Normal file
@ -0,0 +1,14 @@
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|
Welcome to Odte's documentation!
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|
=================================
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|
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|
.. toctree::
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|
:caption: Contents:
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|
:titlesonly:
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|
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|
|
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|
odte
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|
install
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|
hyperparameters
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|
api/index
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|
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|
* :ref:`genindex`
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15
docs/source/install.rst
Normal file
15
docs/source/install.rst
Normal file
@ -0,0 +1,15 @@
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|
Install
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|
=======
|
||||||
|
|
||||||
|
The main stable release
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|
|
||||||
|
``pip install odte``
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|
|
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|
or the last development branch
|
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|
|
||||||
|
``pip install git+https://github.com/doctorado-ml/odte``
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|
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|
Tests
|
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|
*****
|
||||||
|
|
||||||
|
``python -m unittest -v odte.tests``
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17
docs/source/odte.md
Normal file
17
docs/source/odte.md
Normal file
@ -0,0 +1,17 @@
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|
# Odte
|
||||||
|
|
||||||
|
|
||||||
|
[](https://github.com/Doctorado-ML/Odte/actions/workflows/codeql-analysis.yml)
|
||||||
|
[](https://codecov.io/gh/Doctorado-ML/odte)
|
||||||
|
[](https://www.codacy.com/gh/Doctorado-ML/Odte/dashboard?utm_source=github.com&utm_medium=referral&utm_content=Doctorado-ML/Odte&utm_campaign=Badge_Grade)
|
||||||
|
[](https://badge.fury.io/py/Odte)
|
||||||
|
|
||||||
|
[](https://zenodo.org/badge/latestdoi/271595804)
|
||||||
|
|
||||||
|
Oblique Decision Tree Ensemble classifier based on [STree](https://github.com/doctorado-ml/stree) nodes.
|
||||||
|
|
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|
|
||||||
|
|
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|
## License
|
||||||
|
|
||||||
|
Odte is [MIT](https://github.com/doctorado-ml/odte/blob/master/LICENSE) licensed
|
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@ -1,388 +0,0 @@
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{
|
|
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"cells": [
|
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||||||
{
|
|
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"cell_type": "markdown",
|
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"metadata": {},
|
|
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"source": [
|
|
||||||
"# Compare Odte with different estimators"
|
|
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]
|
|
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},
|
|
||||||
{
|
|
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"cell_type": "markdown",
|
|
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"metadata": {},
|
|
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"source": [
|
|
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"# Setup\n",
|
|
||||||
"Uncomment the next cell if Odte is not already installed"
|
|
||||||
]
|
|
||||||
},
|
|
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{
|
|
||||||
"cell_type": "code",
|
|
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"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
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"source": [
|
|
||||||
"#\n",
|
|
||||||
"# Google Colab setup\n",
|
|
||||||
"#\n",
|
|
||||||
"#!pip install git+https://github.com/doctorado-ml/odte\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"import datetime, time\n",
|
|
||||||
"import numpy as np\n",
|
|
||||||
"import pandas as pd\n",
|
|
||||||
"from sklearn.model_selection import train_test_split\n",
|
|
||||||
"from sklearn import tree\n",
|
|
||||||
"from sklearn.metrics import classification_report, confusion_matrix, f1_score\n",
|
|
||||||
"from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
|
|
||||||
"from stree import Stree\n",
|
|
||||||
"from odte import Odte"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"import os\n",
|
|
||||||
"if not os.path.isfile('data/creditcard.csv'):\n",
|
|
||||||
" !wget --no-check-certificate --content-disposition http://nube.jccm.es/index.php/s/Zs7SYtZQJ3RQ2H2/download\n",
|
|
||||||
" !tar xzf creditcard.tgz"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"# Tests"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
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"execution_count": null,
|
|
||||||
"metadata": {
|
|
||||||
"tags": []
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"print(datetime.date.today(), time.strftime(\"%H:%M:%S\"))"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
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"cell_type": "markdown",
|
|
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"metadata": {},
|
|
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"source": [
|
|
||||||
"## Load dataset and normalize values"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
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"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"# Load Dataset\n",
|
|
||||||
"df = pd.read_csv('data/creditcard.csv')\n",
|
|
||||||
"df.shape\n",
|
|
||||||
"random_state = 2020"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
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"execution_count": null,
|
|
||||||
"metadata": {
|
|
||||||
"tags": []
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"print(\"Fraud: {0:.3f}% {1}\".format(df.Class[df.Class == 1].count()*100/df.shape[0], df.Class[df.Class == 1].count()))\n",
|
|
||||||
"print(\"Valid: {0:.3f}% {1:,}\".format(df.Class[df.Class == 0].count()*100/df.shape[0], df.Class[df.Class == 0].count()))"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"# Normalize Amount\n",
|
|
||||||
"from sklearn.preprocessing import RobustScaler\n",
|
|
||||||
"values = RobustScaler().fit_transform(df.Amount.values.reshape(-1, 1))\n",
|
|
||||||
"df['Amount_Scaled'] = values"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {
|
|
||||||
"tags": []
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"# Remove unneeded features\n",
|
|
||||||
"y = df.Class.values\n",
|
|
||||||
"X = df.drop(['Class', 'Time', 'Amount'], axis=1).values\n",
|
|
||||||
"print(f\"X shape: {X.shape}\\ny shape: {y.shape}\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Build the models"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"# Divide dataset\n",
|
|
||||||
"train_size = .7\n",
|
|
||||||
"Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, train_size=train_size, shuffle=True, random_state=random_state, stratify=y)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"# Linear Tree\n",
|
|
||||||
"linear_tree = tree.DecisionTreeClassifier(random_state=random_state)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"# Random Forest\n",
|
|
||||||
"random_forest = RandomForestClassifier(random_state=random_state)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"# Stree\n",
|
|
||||||
"stree = Stree(random_state=random_state, C=.01)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"# AdaBoost\n",
|
|
||||||
"adaboost = AdaBoostClassifier(random_state=random_state)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"# Gradient Boosting\n",
|
|
||||||
"gradient = GradientBoostingClassifier(random_state=random_state)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"# Oblique Decision Tree Ensemble\n",
|
|
||||||
"odte = Odte(random_state=random_state, max_features=\"auto\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Do the test"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"def try_model(name, model):\n",
|
|
||||||
" print(f\"************************** {name} **********************\")\n",
|
|
||||||
" now = time.time()\n",
|
|
||||||
" model.fit(Xtrain, ytrain)\n",
|
|
||||||
" spent = time.time() - now\n",
|
|
||||||
" print(f\"Train Model {name} took: {spent:.4} seconds\")\n",
|
|
||||||
" predict = model.predict(Xtrain)\n",
|
|
||||||
" predictt = model.predict(Xtest)\n",
|
|
||||||
" print(f\"=========== {name} - Train {Xtrain.shape[0]:,} samples =============\",)\n",
|
|
||||||
" print(classification_report(ytrain, predict, digits=6))\n",
|
|
||||||
" print(f\"=========== {name} - Test {Xtest.shape[0]:,} samples =============\")\n",
|
|
||||||
" print(classification_report(ytest, predictt, digits=6))\n",
|
|
||||||
" print(\"Confusion Matrix in Train\")\n",
|
|
||||||
" print(confusion_matrix(ytrain, predict))\n",
|
|
||||||
" print(\"Confusion Matrix in Test\")\n",
|
|
||||||
" print(confusion_matrix(ytest, predictt))\n",
|
|
||||||
" return f1_score(ytest, predictt), spent"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {
|
|
||||||
"tags": []
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"# Train & Test models\n",
|
|
||||||
"models = {\n",
|
|
||||||
" 'Linear Tree':linear_tree, 'Random Forest': random_forest, 'Stree (SVM Tree)': stree, \n",
|
|
||||||
" 'AdaBoost model': adaboost, 'Odte model': odte #'Gradient Boost.': gradient\n",
|
|
||||||
"}\n",
|
|
||||||
"\n",
|
|
||||||
"best_f1 = 0\n",
|
|
||||||
"outcomes = []\n",
|
|
||||||
"for name, model in models.items():\n",
|
|
||||||
" f1, time_spent = try_model(name, model)\n",
|
|
||||||
" outcomes.append((name, f1, time_spent))\n",
|
|
||||||
" if f1 > best_f1:\n",
|
|
||||||
" best_model = name\n",
|
|
||||||
" best_time = time_spent\n",
|
|
||||||
" best_f1 = f1"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {
|
|
||||||
"tags": []
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"print(\"*\"*110)\n",
|
|
||||||
"print(f\"*The best f1 model is {best_model}, with a f1 score: {best_f1:.4} in {best_time:.6} seconds with {train_size:,} samples in train dataset\")\n",
|
|
||||||
"print(\"*\"*110)\n",
|
|
||||||
"for name, f1, time_spent in outcomes:\n",
|
|
||||||
" print(f\"Model: {name}\\t Time: {time_spent:6.2f} seconds\\t f1: {f1:.4}\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "raw",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"**************************************************************************************************************\n",
|
|
||||||
"*The best f1 model is Random Forest, with a f1 score: 0.8815 in 152.54 seconds with 0.7 samples in train dataset\n",
|
|
||||||
"**************************************************************************************************************\n",
|
|
||||||
"Model: Linear Tree\t Time: 13.52 seconds\t f1: 0.7645\n",
|
|
||||||
"Model: Random Forest\t Time: 152.54 seconds\t f1: 0.8815\n",
|
|
||||||
"Model: Stree (SVM Tree)\t Time: 32.55 seconds\t f1: 0.8603\n",
|
|
||||||
"Model: AdaBoost model\t Time: 47.34 seconds\t f1: 0.7509\n",
|
|
||||||
"Model: Gradient Boost.\t Time: 244.12 seconds\t f1: 0.5259"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"```\n",
|
|
||||||
"******************************************************************************************************************\n",
|
|
||||||
"*The best f1 model is Random Forest, with a f1 score: 0.8815 in 218.966 seconds with 0.7 samples in train dataset\n",
|
|
||||||
"******************************************************************************************************************\n",
|
|
||||||
"Model: Linear Tree Time: 23.05 seconds\t f1: 0.7645\n",
|
|
||||||
"Model: Random Forest\t Time: 218.97 seconds\t f1: 0.8815\n",
|
|
||||||
"Model: Stree (SVM Tree)\t Time: 49.45 seconds\t f1: 0.8467\n",
|
|
||||||
"Model: AdaBoost model\t Time: 73.83 seconds\t f1: 0.7509\n",
|
|
||||||
"Model: Gradient Boost.\t Time: 388.69 seconds\t f1: 0.5259\n",
|
|
||||||
"Model: Neural Network\t Time: 25.47 seconds\t f1: 0.8328\n",
|
|
||||||
"Model: Odte \t Time:2134.25 seconds\t f1: 0.8385\n",
|
|
||||||
"```"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"metadata": {
|
|
||||||
"hide_input": false,
|
|
||||||
"kernelspec": {
|
|
||||||
"display_name": "Python 3.7.6 64-bit ('general': venv)",
|
|
||||||
"language": "python",
|
|
||||||
"name": "python37664bitgeneralvenvfbd0a23e74cf4e778460f5ffc6761f39"
|
|
||||||
},
|
|
||||||
"language_info": {
|
|
||||||
"codemirror_mode": {
|
|
||||||
"name": "ipython",
|
|
||||||
"version": 3
|
|
||||||
},
|
|
||||||
"file_extension": ".py",
|
|
||||||
"mimetype": "text/x-python",
|
|
||||||
"name": "python",
|
|
||||||
"nbconvert_exporter": "python",
|
|
||||||
"pygments_lexer": "ipython3",
|
|
||||||
"version": "3.7.6-final"
|
|
||||||
},
|
|
||||||
"toc": {
|
|
||||||
"base_numbering": 1,
|
|
||||||
"nav_menu": {},
|
|
||||||
"number_sections": true,
|
|
||||||
"sideBar": true,
|
|
||||||
"skip_h1_title": false,
|
|
||||||
"title_cell": "Table of Contents",
|
|
||||||
"title_sidebar": "Contents",
|
|
||||||
"toc_cell": false,
|
|
||||||
"toc_position": {},
|
|
||||||
"toc_section_display": true,
|
|
||||||
"toc_window_display": false
|
|
||||||
},
|
|
||||||
"varInspector": {
|
|
||||||
"cols": {
|
|
||||||
"lenName": 16,
|
|
||||||
"lenType": 16,
|
|
||||||
"lenVar": 40
|
|
||||||
},
|
|
||||||
"kernels_config": {
|
|
||||||
"python": {
|
|
||||||
"delete_cmd_postfix": "",
|
|
||||||
"delete_cmd_prefix": "del ",
|
|
||||||
"library": "var_list.py",
|
|
||||||
"varRefreshCmd": "print(var_dic_list())"
|
|
||||||
},
|
|
||||||
"r": {
|
|
||||||
"delete_cmd_postfix": ") ",
|
|
||||||
"delete_cmd_prefix": "rm(",
|
|
||||||
"library": "var_list.r",
|
|
||||||
"varRefreshCmd": "cat(var_dic_list()) "
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"position": {
|
|
||||||
"height": "392px",
|
|
||||||
"left": "1518px",
|
|
||||||
"right": "20px",
|
|
||||||
"top": "40px",
|
|
||||||
"width": "392px"
|
|
||||||
},
|
|
||||||
"types_to_exclude": [
|
|
||||||
"module",
|
|
||||||
"function",
|
|
||||||
"builtin_function_or_method",
|
|
||||||
"instance",
|
|
||||||
"_Feature"
|
|
||||||
],
|
|
||||||
"window_display": true
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 4
|
|
||||||
}
|
|
||||||
@ -1,174 +0,0 @@
|
|||||||
{
|
|
||||||
"cells": [
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"import datetime, time\n",
|
|
||||||
"import numpy as np\n",
|
|
||||||
"import pandas as pd\n",
|
|
||||||
"from sklearn.model_selection import train_test_split, cross_validate\n",
|
|
||||||
"from sklearn import tree\n",
|
|
||||||
"from sklearn.metrics import classification_report, confusion_matrix, f1_score\n",
|
|
||||||
"from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, BaggingClassifier\n",
|
|
||||||
"from stree import Stree\n",
|
|
||||||
"from odte import Odte\n",
|
|
||||||
"\n",
|
|
||||||
"random_state = 1"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from sklearn.datasets import load_wine\n",
|
|
||||||
"X, y = load_wine(return_X_y=True)\n",
|
|
||||||
"Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=.2, random_state=random_state)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"n_estimators = 20\n",
|
|
||||||
"clf = {}\n",
|
|
||||||
"clf[\"stree\"] = Stree(random_state=random_state, max_depth=5)\n",
|
|
||||||
"clf[\"stree\"].set_params(**dict(splitter=\"best\", kernel=\"linear\", max_features=\"auto\"))\n",
|
|
||||||
"clf[\"odte\"] = Odte(n_jobs=-1, estimator=clf[\"stree\"], random_state=random_state, n_estimators=n_estimators, max_features=.8)\n",
|
|
||||||
"clf[\"adaboost\"] = AdaBoostClassifier(estimator=clf[\"stree\"], n_estimators=n_estimators, random_state=random_state, algorithm=\"SAMME\")\n",
|
|
||||||
"clf[\"bagging\"] = BaggingClassifier(estimator=clf[\"stree\"], n_estimators=n_estimators)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {
|
|
||||||
"tags": []
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"print(\"*\"*30,\"Results for wine\", \"*\"*30)\n",
|
|
||||||
"for clf_type, item in clf.items():\n",
|
|
||||||
" print(f\"Training {clf_type}...\")\n",
|
|
||||||
" now = time.time()\n",
|
|
||||||
" item.fit(Xtrain, ytrain)\n",
|
|
||||||
" print(f\"Score: {item.score(Xtest, ytest) * 100:.3f} in {time.time()-now:.2f} seconds\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from sklearn.datasets import load_iris\n",
|
|
||||||
"X, y = load_iris(return_X_y=True)\n",
|
|
||||||
"Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=.2, random_state=random_state)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"n_estimators = 10\n",
|
|
||||||
"clf = {}\n",
|
|
||||||
"clf[\"stree\"] = Stree(random_state=random_state, max_depth=3)\n",
|
|
||||||
"clf[\"odte\"] = Odte(n_jobs=-1, random_state=random_state, n_estimators=n_estimators, max_features=1.0)\n",
|
|
||||||
"clf[\"adaboost\"] = AdaBoostClassifier(estimator=clf[\"stree\"], n_estimators=n_estimators, random_state=random_state, algorithm=\"SAMME\")\n",
|
|
||||||
"clf[\"bagging\"] = BaggingClassifier(estimator=clf[\"stree\"], n_estimators=n_estimators)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {
|
|
||||||
"tags": []
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"print(\"*\"*30,\"Results for iris\", \"*\"*30)\n",
|
|
||||||
"for clf_type, item in clf.items():\n",
|
|
||||||
" print(f\"Training {clf_type}...\")\n",
|
|
||||||
" now = time.time()\n",
|
|
||||||
" item.fit(Xtrain, ytrain)\n",
|
|
||||||
" print(f\"Score: {item.score(Xtest, ytest) * 100:.3f} in {time.time()-now:.2f} seconds\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {
|
|
||||||
"tags": []
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"cross = cross_validate(estimator=clf[\"odte\"], X=X, y=y, n_jobs=-1, return_train_score=True)\n",
|
|
||||||
"print(cross)\n",
|
|
||||||
"print(f\"{np.mean(cross['test_score'])*100:.3f} +- {np.std(cross['test_score']):.3f}\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {
|
|
||||||
"tags": []
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"cross = cross_validate(estimator=clf[\"adaboost\"], X=X, y=y, n_jobs=-1, return_train_score=True)\n",
|
|
||||||
"print(cross)\n",
|
|
||||||
"print(f\"{np.mean(cross['test_score'])*100:.3f} +- {np.std(cross['test_score']):.3f}\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {
|
|
||||||
"tags": []
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from sklearn.utils.estimator_checks import check_estimator\n",
|
|
||||||
"# Make checks one by one\n",
|
|
||||||
"c = 0\n",
|
|
||||||
"checks = check_estimator(Odte(), generate_only=True)\n",
|
|
||||||
"for check in checks:\n",
|
|
||||||
" c += 1\n",
|
|
||||||
" print(c, check[1])\n",
|
|
||||||
" check[1](check[0])"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"metadata": {
|
|
||||||
"interpreter": {
|
|
||||||
"hash": "da86226729227d0e8962a5ec29ea906307507ca2c30ceaaf651c09a617630939"
|
|
||||||
},
|
|
||||||
"kernelspec": {
|
|
||||||
"display_name": "Python 3.9.2 64-bit ('general': venv)",
|
|
||||||
"name": "python3"
|
|
||||||
},
|
|
||||||
"language_info": {
|
|
||||||
"codemirror_mode": {
|
|
||||||
"name": "ipython",
|
|
||||||
"version": 3
|
|
||||||
},
|
|
||||||
"file_extension": ".py",
|
|
||||||
"mimetype": "text/x-python",
|
|
||||||
"name": "python",
|
|
||||||
"nbconvert_exporter": "python",
|
|
||||||
"pygments_lexer": "ipython3",
|
|
||||||
"version": "3.9.7"
|
|
||||||
},
|
|
||||||
"orig_nbformat": 2
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 2
|
|
||||||
}
|
|
||||||
19
odte/Odte.py
19
odte/Odte.py
@ -33,10 +33,10 @@ class Odte(BaseEnsemble, ClassifierMixin):
|
|||||||
# n_jobs = -1 to use all available cores
|
# n_jobs = -1 to use all available cores
|
||||||
n_jobs: int = -1,
|
n_jobs: int = -1,
|
||||||
estimator: BaseEstimator = Stree(),
|
estimator: BaseEstimator = Stree(),
|
||||||
random_state: int = 0,
|
random_state: Optional[int] = None,
|
||||||
max_features: Optional[Union[str, int, float]] = None,
|
max_features: Optional[Union[str, int, float]] = None,
|
||||||
max_samples: Optional[Union[int, float]] = None,
|
max_samples: Optional[Union[int, float]] = None,
|
||||||
n_estimators: int = 10,
|
n_estimators: int = 100,
|
||||||
be_hyperparams: str = "{}",
|
be_hyperparams: str = "{}",
|
||||||
):
|
):
|
||||||
super().__init__(
|
super().__init__(
|
||||||
@ -62,7 +62,10 @@ class Odte(BaseEnsemble, ClassifierMixin):
|
|||||||
)
|
)
|
||||||
|
|
||||||
def fit(
|
def fit(
|
||||||
self, X: np.ndarray, y: np.ndarray, sample_weight: np.ndarray = None
|
self,
|
||||||
|
X: np.ndarray,
|
||||||
|
y: np.ndarray,
|
||||||
|
sample_weight: Optional[np.ndarray] = None,
|
||||||
) -> Odte:
|
) -> Odte:
|
||||||
# Check parameters are Ok.
|
# Check parameters are Ok.
|
||||||
if self.n_estimators < 3:
|
if self.n_estimators < 3:
|
||||||
@ -100,9 +103,6 @@ class Odte(BaseEnsemble, ClassifierMixin):
|
|||||||
tdepth += depth
|
tdepth += depth
|
||||||
tnodes += nodes
|
tnodes += nodes
|
||||||
tleaves += leaves
|
tleaves += leaves
|
||||||
# self.depth_ = tdepth / self.n_estimators
|
|
||||||
# self.leaves_ = tleaves / self.n_estimators
|
|
||||||
# self.nodes_ = tnodes / self.n_estimators
|
|
||||||
self.depth_ = tdepth
|
self.depth_ = tdepth
|
||||||
self.leaves_ = tleaves
|
self.leaves_ = tleaves
|
||||||
self.nodes_ = tnodes
|
self.nodes_ = tnodes
|
||||||
@ -113,6 +113,11 @@ class Odte(BaseEnsemble, ClassifierMixin):
|
|||||||
n_samples = X.shape[0]
|
n_samples = X.shape[0]
|
||||||
boot_samples = self._get_bootstrap_n_samples(n_samples)
|
boot_samples = self._get_bootstrap_n_samples(n_samples)
|
||||||
estimator = clone(self.estimator_)
|
estimator = clone(self.estimator_)
|
||||||
|
defined_state = (
|
||||||
|
random.randint(0, 2**31)
|
||||||
|
if self.random_state is None
|
||||||
|
else self.random_state
|
||||||
|
)
|
||||||
return Parallel(n_jobs=self.n_jobs, prefer="threads")( # type: ignore
|
return Parallel(n_jobs=self.n_jobs, prefer="threads")( # type: ignore
|
||||||
delayed(Odte._parallel_build_tree)(
|
delayed(Odte._parallel_build_tree)(
|
||||||
estimator,
|
estimator,
|
||||||
@ -125,7 +130,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
|
|||||||
self.be_hyperparams,
|
self.be_hyperparams,
|
||||||
)
|
)
|
||||||
for random_seed in range(
|
for random_seed in range(
|
||||||
self.random_state, self.random_state + self.n_estimators
|
defined_state, defined_state + self.n_estimators
|
||||||
)
|
)
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|||||||
@ -1,3 +1,4 @@
|
|||||||
|
from ._version import __version__
|
||||||
from .Odte import Odte
|
from .Odte import Odte
|
||||||
|
|
||||||
__author__ = "Ricardo Montañana Gómez"
|
__author__ = "Ricardo Montañana Gómez"
|
||||||
@ -5,4 +6,4 @@ __copyright__ = "Copyright 2020-2021, Ricardo Montañana Gómez"
|
|||||||
__license__ = "MIT License"
|
__license__ = "MIT License"
|
||||||
__author_email__ = "ricardo.montanana@alu.uclm.es"
|
__author_email__ = "ricardo.montanana@alu.uclm.es"
|
||||||
|
|
||||||
__all__ = ["Odte"]
|
__all__ = ["__version__", "Odte"]
|
||||||
|
|||||||
@ -1 +1 @@
|
|||||||
__version__ = "0.3.6"
|
__version__ = "1.0.0"
|
||||||
|
|||||||
@ -180,7 +180,7 @@ class Odte_test(unittest.TestCase):
|
|||||||
warnings.filterwarnings("ignore", category=RuntimeWarning)
|
warnings.filterwarnings("ignore", category=RuntimeWarning)
|
||||||
from sklearn.utils.estimator_checks import check_estimator
|
from sklearn.utils.estimator_checks import check_estimator
|
||||||
|
|
||||||
check_estimator(Odte())
|
check_estimator(Odte(n_estimators=10))
|
||||||
|
|
||||||
def test_nodes_leaves_not_fitted(self):
|
def test_nodes_leaves_not_fitted(self):
|
||||||
tclf = Odte(
|
tclf = Odte(
|
||||||
|
|||||||
@ -1,4 +1,3 @@
|
|||||||
# type: ignore
|
|
||||||
from .Odte_tests import Odte_test
|
from .Odte_tests import Odte_test
|
||||||
|
|
||||||
__all__ = ["Odte_test"]
|
__all__ = ["Odte_test"]
|
||||||
|
|||||||
@ -1,5 +1,64 @@
|
|||||||
|
[build-system]
|
||||||
|
requires = ["hatchling"]
|
||||||
|
build-backend = "hatchling.build"
|
||||||
|
|
||||||
|
[project]
|
||||||
|
name = "Odte"
|
||||||
|
description = "Oblique decision tree Ensemble."
|
||||||
|
readme = "README.md"
|
||||||
|
license = { file = "LICENSE" }
|
||||||
|
authors = [
|
||||||
|
{ name = "Ricardo Montañana", email = "ricardo.montanana@alu.uclm.es" },
|
||||||
|
]
|
||||||
|
dynamic = ['version']
|
||||||
|
dependencies = ["stree>=1.4"]
|
||||||
|
requires-python = ">=3.11"
|
||||||
|
keywords = [
|
||||||
|
"scikit-learn",
|
||||||
|
"oblique-classifier",
|
||||||
|
"oblique-decision-tree",
|
||||||
|
"decision-tree",
|
||||||
|
"ensemble",
|
||||||
|
"svm",
|
||||||
|
]
|
||||||
|
classifiers = [
|
||||||
|
"Development Status :: 5 - Production/Stable",
|
||||||
|
"Intended Audience :: Science/Research",
|
||||||
|
"Intended Audience :: Developers",
|
||||||
|
"Topic :: Software Development",
|
||||||
|
"Topic :: Scientific/Engineering",
|
||||||
|
"License :: OSI Approved :: MIT License",
|
||||||
|
"Natural Language :: English",
|
||||||
|
"Operating System :: OS Independent",
|
||||||
|
"Programming Language :: Python :: 3.11",
|
||||||
|
"Programming Language :: Python :: 3.12",
|
||||||
|
]
|
||||||
|
|
||||||
|
[project.optional-dependencies]
|
||||||
|
dev = ["black", "flake8", "coverage", "mypy", "pandas", "hatch", "pip-audit"]
|
||||||
|
doc = ["sphinx", "myst-parser", "sphinx_rtd_theme", "sphinx-autodoc-typehints"]
|
||||||
|
|
||||||
|
[project.urls]
|
||||||
|
Home = "https://github.com/doctorado-ml/odte"
|
||||||
|
|
||||||
|
[tool.hatch.version]
|
||||||
|
path = "odte/_version.py"
|
||||||
|
|
||||||
|
[tool.mypy]
|
||||||
|
exclude = ['tests']
|
||||||
|
|
||||||
|
[tool.coverage.run]
|
||||||
|
branch = true
|
||||||
|
source = ["odte"]
|
||||||
|
command_line = "-m unittest discover -s odte.tests"
|
||||||
|
|
||||||
|
[tool.coverage.report]
|
||||||
|
show_missing = true
|
||||||
|
fail_under = 100
|
||||||
|
|
||||||
[tool.black]
|
[tool.black]
|
||||||
line-length = 79
|
line-length = 79
|
||||||
|
target_version = ['py311']
|
||||||
include = '\.pyi?$'
|
include = '\.pyi?$'
|
||||||
exclude = '''
|
exclude = '''
|
||||||
/(
|
/(
|
||||||
|
|||||||
@ -1 +1 @@
|
|||||||
stree>=1.3.1
|
stree>=1.4
|
||||||
|
|||||||
48
setup.py
48
setup.py
@ -1,48 +0,0 @@
|
|||||||
import setuptools
|
|
||||||
import os
|
|
||||||
|
|
||||||
|
|
||||||
def readme():
|
|
||||||
with open("README.md") as f:
|
|
||||||
return f.read()
|
|
||||||
|
|
||||||
|
|
||||||
def get_data(field):
|
|
||||||
item = ""
|
|
||||||
file_name = "_version.py" if field == "version" else "__init__.py"
|
|
||||||
with open(os.path.join("odte", file_name)) as f:
|
|
||||||
for line in f.readlines():
|
|
||||||
if line.startswith(f"__{field}__"):
|
|
||||||
delim = '"' if '"' in line else "'"
|
|
||||||
item = line.split(delim)[1]
|
|
||||||
break
|
|
||||||
else:
|
|
||||||
raise RuntimeError(f"Unable to find {field} string.")
|
|
||||||
return item
|
|
||||||
|
|
||||||
|
|
||||||
setuptools.setup(
|
|
||||||
name="Odte",
|
|
||||||
version=get_data("version"),
|
|
||||||
license=get_data("license"),
|
|
||||||
description="Oblique decision tree Ensemble",
|
|
||||||
long_description=readme(),
|
|
||||||
long_description_content_type="text/markdown",
|
|
||||||
packages=setuptools.find_packages(),
|
|
||||||
url="https://github.com/doctorado-ml/odte",
|
|
||||||
author=get_data("author"),
|
|
||||||
author_email=get_data("author_email"),
|
|
||||||
keywords="scikit-learn oblique-classifier oblique-decision-tree decision-\
|
|
||||||
tree ensemble svm svc",
|
|
||||||
classifiers=[
|
|
||||||
"Development Status :: 4 - Beta",
|
|
||||||
"License :: OSI Approved :: MIT License",
|
|
||||||
"Programming Language :: Python :: 3.8",
|
|
||||||
"Natural Language :: English",
|
|
||||||
"Topic :: Scientific/Engineering :: Artificial Intelligence",
|
|
||||||
"Intended Audience :: Science/Research",
|
|
||||||
],
|
|
||||||
install_requires=["stree"],
|
|
||||||
test_suite="odte.tests",
|
|
||||||
zip_safe=False,
|
|
||||||
)
|
|
||||||
Loading…
x
Reference in New Issue
Block a user